Literature DB >> 33465154

Recovery patterns and physics of the network.

Alireza Ermagun1, Nazanin Tajik2.   

Abstract

In a progressively interconnected world, the loss of system resilience has consequences for human health, the economy, and the environment. Research has exploited the science of networks to explain the resilience of complex systems against random attacks, malicious attacks, and the localized attacks induced by natural disasters or mass attacks. Little is known about the elucidation of system recovery by the network topology. This study adds to the knowledge of network resilience by examining the nexus of recoverability and network topology. We establish a new paradigm for identifying the recovery behavior of networks and introduce the recoverability measure. Results indicate that the recovery response behavior and the recoverability measure are the function of both size and topology of networks. In small sized networks, the return to recovery exhibits homogeneous recovery behavior over topology, while the return shape is dispersed with an increase in the size of network. A network becomes more recoverable as connectivity measures of the network increase, and less recoverable as accessibility measures of network increase. Overall, the results not only offer guidance on designing recoverable networks, but also depict the recovery nature of networks deliberately following a disruption. Our recovery behavior and recoverability measure has been tested on 16 distinct network topologies. The relevant recovery behavior can be generalized based on our definition for any network topology recovering deliberately.

Entities:  

Year:  2021        PMID: 33465154      PMCID: PMC7815135          DOI: 10.1371/journal.pone.0245396

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  25 in total

1.  Genomic analysis of regulatory network dynamics reveals large topological changes.

Authors:  Nicholas M Luscombe; M Madan Babu; Haiyuan Yu; Michael Snyder; Sarah A Teichmann; Mark Gerstein
Journal:  Nature       Date:  2004-09-16       Impact factor: 49.962

2.  Catastrophic cascade of failures in interdependent networks.

Authors:  Sergey V Buldyrev; Roni Parshani; Gerald Paul; H Eugene Stanley; Shlomo Havlin
Journal:  Nature       Date:  2010-04-15       Impact factor: 49.962

3.  Uncovering the overlapping community structure of complex networks in nature and society.

Authors:  Gergely Palla; Imre Derényi; Illés Farkas; Tamás Vicsek
Journal:  Nature       Date:  2005-06-09       Impact factor: 49.962

4.  Hierarchical structure and the prediction of missing links in networks.

Authors:  Aaron Clauset; Cristopher Moore; M E J Newman
Journal:  Nature       Date:  2008-05-01       Impact factor: 49.962

5.  Sequential defense against random and intentional attacks in complex networks.

Authors:  Pin-Yu Chen; Shin-Ming Cheng
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2015-02-06

6.  Self-organized patchiness in asthma as a prelude to catastrophic shifts.

Authors:  Jose G Venegas; Tilo Winkler; Guido Musch; Marcos F Vidal Melo; Dominick Layfield; Nora Tgavalekos; Alan J Fischman; Ronald J Callahan; Giacomo Bellani; R Scott Harris
Journal:  Nature       Date:  2005-03-16       Impact factor: 49.962

7.  Reconstructing missing complex networks against adversarial interventions.

Authors:  Yuankun Xue; Paul Bogdan
Journal:  Nat Commun       Date:  2019-04-15       Impact factor: 14.919

8.  Robustness and Vulnerability of Networks with Dynamical Dependency Groups.

Authors:  Ya-Nan Bai; Ning Huang; Lei Wang; Zhi-Xi Wu
Journal:  Sci Rep       Date:  2016-11-28       Impact factor: 4.379

9.  Optimal percolation on multiplex networks.

Authors:  Saeed Osat; Ali Faqeeh; Filippo Radicchi
Journal:  Nat Commun       Date:  2017-11-16       Impact factor: 14.919

10.  Recovery of infrastructure networks after localised attacks.

Authors:  Fuyu Hu; Chi Ho Yeung; Saini Yang; Weiping Wang; An Zeng
Journal:  Sci Rep       Date:  2016-04-14       Impact factor: 4.379

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